Computer Vision Emotion Detection App A Python-based application for detecting emotions using computer vision, TensorFlow, Keras, and Tkinter.

Overview The Computer Vision Emotion Detection App is a machine learning-based application that detects facial emotions in real-time video streams or images. This app leverages the power of deep learning frameworks, TensorFlow and Keras, to classify emotions accurately. The user-friendly interface is built using Tkinter, making it easy to interact with and visualize emotion detection results.

Features Real-time Emotion Detection: Identifies emotions from a live webcam feed. Image-based Emotion Analysis: Allows users to upload images for emotion classification. Emotion Classes: Detects emotions like happy, sad, angry, surprised, neutral, and more. Graphical User Interface (GUI): Built with Tkinter for seamless user interaction. Visualization: Displays detected emotions with their respective confidence scores. Technologies Used Python: Core programming language for application development. TensorFlow: Framework for building and deploying deep learning models. Keras: Simplified API for defining and training neural networks. OpenCV: Real-time computer vision library for face detection and preprocessing. Tkinter: Python library for creating the graphical user interface. How It Works Face Detection: OpenCV detects faces in the video or image. Preprocessing: Detected face regions are cropped, resized, and normalized for model input. Emotion Classification: A pre-trained deep learning model classifies the emotion from the facial features. Results Visualization: The detected emotion and confidence level are displayed in real-time via the GUI. Requirements Python 3.8+ TensorFlow 2.x Keras 3.x OpenCV Tkinter (bundled with Python)